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Record W2285725703 · doi:10.4271/2004-01-3086

Reliability of Electronic Engine Controls

2004· article· en· W2285725703 on OpenAlexaff
Sharanpal S. Sikand, Sham S. Hariram

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2004
Typearticle
Languageen
FieldEngineering
TopicReal-time simulation and control systems
Canadian institutionsBoeing (Canada)
Fundersnot available
KeywordsReliability (semiconductor)Automotive engineeringReliability engineeringComputer scienceEngineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Reliability of Electronic Engine Controls (EEC) for airplane jet engines is critical for reliable and economic operations of the airplane. EEC failures can cause in-flight engine shut downs and airplane dispatch delays. The economic impact of this shortcoming is very large on the airlines, engine suppliers, and airplane manufacturers.</div> <div class="htmlview paragraph">The EEC is one of the most complex and expensive components of the jet engine. Reliability must be designed into the EEC from the initial stage of design by consideration of the environment, hardware selection, manufacturing processes, software design, rigorous testing, fault detection and monitoring logic, and proper in-service trouble shooting procedures.</div> <div class="htmlview paragraph">This paper outlines an approach for designing a high reliability EEC and includes a novel design for controlling EEC cooling.</div>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.215
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2004
Admission routes1
Has abstractyes

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